Multimodal dynamic characterization of materials under programmable environments and environment prediction

ABSTRACT

An integrated multifunctional environmental characterization system (IMECS) is provided. The IMECS may comprises a memory, one or more interfaces and a processor. The processor may be configured to predict an environment condition adjacent to a thin film using one or more machine learned models from one or more measured properties of the thin film received via the one or more interfaces; and/or predict values for one or more properties of the thin film using the one or more machine learned models from an environmental condition received via one of the one or more interfaces; and display the predicted environment condition and/or the predicted one or more properties. The processor may also adjust the acquisition parameters used to acquire values of one or more properties of the thin film from received acquisition parameters via a user interface based on measured values for the same properties.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of and priority to U.S. Provisional Application Ser. No. 63/081,959 filed on Sep. 23, 2020 and U.S. Provisional Application Ser. No. 63/081,962 filed on Sep. 23, 2020, the entirety of which are incorporated by reference.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

The United States Government has rights in this invention pursuant to contract no. DE-AC05-00OR22725 between the United States Department of Energy and UT-Battelle, LLC.

FIELD OF THE DISCLOSURE

This disclosure relates to characterization of a material using multiple modalities in one or more environments. This disclosure also relates to predicting one or more properties of materials or environmental conditions based on the characterization of the material using one or more modalities at a set of other environmental conditions.

BACKGROUND

Materials may be characterized using one or more modalities. For example, the modalities may be electrical, optical, gravimetric and viscoelastic, and mechanical. In a known system, when multiple modalities are to be tested, an operator initially provides acquisition parameters for one of the modalities and subsequently acquires the measurements using the acquisition parameters. Afterwards, the measurements are processed including any calculations of other properties, interpreted, and displayed. This process may be repeated with other acquisition parameters for the same modality. Once one of the modalities characterizations is completed, another modality is separately tested, data acquired and processed.

However, in a typical system, the acquisition parameters are set in advanced and are not adjusted dynamically based on previous measurement responses (or calculated) or environmental conditions. For example, a known quartz crystal micro-balance with Dissipation Mode (QCM-D) uses a single frequency window of all QCM-D frequencies and with a set resolution. However, the use of a single frequency window that is large (with a high resolution) has a long data acquisition time to acquire frequency data over the large single frequency window. Additionally, since there is a frequency shift, and peak broadening at higher overtones (harmonics), there may be a loss of the full peak or an incorrect fitting where a peak is not completely detected. Alternatively, a reduction in the resolution, e.g., number of frequencies acquired within the single frequency window, leads to a loss of sensitivity.

Moreover, under certain environmental conditions, such as relative humidity, there may significantly decrease the intensity and position of resonant peaks in certain harmonics, which may result in peaks not being detected using the narrow single frequency window and a fixed frequency resolution. Additionally, certain overtones may have spurious peaks. The intensity of these spurious peaks may be significantly lower than its harmonic peak which may result in spurious peaks not being detected using the single frequency window and a fixed resolution.

Given that certain harmonic peaks and spurious peaks may not be properly detected, a material have a thickness M, may not be properly characterized. For example, different harmonics have different penetration depths in a material such as a film or liquid. By missing one or more harmonics, information regarding its associated visco-elastic properties at this thickness may also be missing. For example, higher crystal harmonics probe closer to the film-crystal-interface, while lower crystal harmonics probe closer to the film-environment interface.

When the acquisition parameters for testing modality may be changed in a characterization system, it generally requires an experienced researcher for selection of reasonable experimental parameters, selection of data processing and analytic tools and approaches, and interpretation of experimental results, which presents a significant limitation, and oftentimes leads to multiple partially finished redundant experiments/tests. For instance, the task of characterizing the changes of a single material functionality (i.e. electrical) in response to environment requires researcher input for instruments and data acquisition (integration time, sampling rates, biases), programming a series of environmental conditions inside the sample chamber (incident light intensity, pressure, temperature, humidity, gas/vapor pressure and composition), and specifying software protocols for data processing and modeling (typically performed after the experiment). However, optimizing the acquisition parameters is difficult, especially for materials which exhibit non-linear, heterogeneous or switching (for instance going from low conductivity state to high conductivity state) responses. Additionally, adjustment of experiment settings on the fly commonly leads to a series of redundant experiments and is prohibitively time-consuming in R&D workflows. Testing is commonly done on different instruments (electro-optical viscoelastic, gravimetric measurements would require 4 different instruments) the data processing routines are difficult/impossible to couple due because of proprietary software used in instruments where the functionality is measured. If the parameter of the modality measurement is selected incorrectly, the entire experiment needs to be redone. If modalities are measured on different instruments, there may be difference between the environments, for instance different size of the environmental compartment which leads to different time for environment stabilization. Such difference will hamper comparison between dynamic response of the materials, and thus loss of important information. Moreover, during measurement of single functionality researcher does cannot access correlation of properties between multimodal response, and thus can not conclude if the test parameters environmental conditions are set correctly. Also, research does not have capability to access if the test is a success. This information becomes available after post-experiment data processing. Thus, the feed back loop part (enabling selection of correct environmental conditions or parameters of the modality test) is delayed by hours or days. Such situation slows the development of novel functionalities and limit testing of novel material compositions.

Moreover, for a known system each modality may require specific format of substrate and film thickness for testing. Optical, electrical, viscoelastic and gravimetric may require up to four separate substrates and four different films.

SUMMARY

Accordingly, disclosed is an integrated multifunctional environmental characterization system (IMECS). The IMECS may comprise a memory, one or more interfaces and a processor. The memory may be configured to store one or more machine learned models correlating one or more environmental conditions adjacent a thin film with one or more properties of the thin film. The one or more properties may comprise one or more properties from at least one gravimetric/viscoelastic, electrical or optical properties groups. The processor may be configured to predict an environment condition adjacent to the thin film using the one or more machine learned models from one or more measured properties of the thin film received via the one or more interfaces; and/or predict values for one or more properties of the thin film using the one or more machine learned models from an environmental condition received via one of the one or more interfaces; and display the predicted environment condition and/or the predicted one or more properties.

In an aspect of the disclosure, a sample of the thin film may be deposited on a quartz crystal. Electrodes may be disposed on the quartz crystal to be at least partially covered by the thin film. The electrodes may be for QCM measurements or electrical measurements. The optical measurements may be done in back reflection mode when the probe light is reflected of the surface of QCM electrode (passing through a film twice which leads to improved sensitivity) or in transmittance mode, when the probe light passes through the film of interest and transparent part of the QCM crystal.

Also disclosed is a fluid-flow cell (flow cell). The flow cell may be arranged to encompass the quartz crystal and the thin film. The flow cell may be configured to maintain a controlled environmental condition around the quartz crystal and the thin film and circulate a fluid adjacent to the thin film in conjunction with the one or more environmental control modules. The fluid may comprise gas, vapor, liquid and/or a combination thereof.

In an aspect of the disclosure, the flow cell may enable simultaneous measurement of electrical, optical and gravimetric/viscoelastic properties.

In other aspects, disclosed is an IMECS which may comprise a quartz-crystal microbalance (QCM), electrodes, a user interface, and a processor. The QCM may comprise a quartz crystal having a face onto which a thin film is deposited. The electrodes may be disposed on the face of the quartz crystal to be at least partially covered by the thin film. The user interface may be configured to receive acquisition parameters for two or more characterization modules. The characterization modules may be selected from a group consisting of an optical characterization module, an electrical characterization module and a gravimetric/viscoelastic characterization module. The processor may be configured to adjust the acquisition parameters used to acquire values of one or more properties of the thin film by each respective characterization module from the received acquisition parameters via the user interface based on measured values for the same properties.

BRIEF DESCRIPTION OF THE DRAWINGS

The file of this patent contains at least one drawing executed in color. Copies of this patent with color drawing(s) will be provided by the Patent and Trademark Office upon request and payment of the necessary fee.

FIG. 1 illustrates a diagram of multiple characterization modules and a multi-modal characterization system in accordance with aspects of the disclosure;

FIG. 2 illustrates a diagram of a processing system in accordance with aspects of the disclosure;

FIG. 3 illustrates a diagram showing the frequency range of characterization in accordance with aspects of the disclosure;

FIG. 4 illustrates a schematic representation of the multi-modal characterization in accordance with aspects of the disclosure;

FIG. 5 illustrates an example of a user interface in accordance with aspects of the disclosure;

FIGS. 6A and 6B illustrate views of an example flow cell in accordance with aspects of the disclosure and FIGS. 6C and 6D show schematic views of two different electrode configurations for electrical measurements;

FIG. 7 illustrates a flow chart for a method in accordance with aspects of the disclosure;

FIG. 8 illustrates a diagram of harmonics and a relative location of the measurement of the harmonics;

FIG. 9A illustrates an example of a dynamic sampling window in accordance with aspects of the disclosure;

FIG. 9B illustrates an example of automated detection, tracking and prediction of spectral peak positions of main resonance and spurious overtones in accordance with aspects of the disclosure;

FIG. 9C illustrates an example of automated multi-peak deconvolution and fitting in accordance with aspects of the disclosure;

FIG. 9D illustrates an example of selective peak fitting in accordance with aspects of the disclosure;

FIG. 9E illustrates an example of predicting future frequency shifts in accordance with aspects of the disclosure;

FIG. 9F illustrates an example of estimation of event detection probability based on dynamics of sample response with low SNR in accordance with aspects of the disclosure;

FIGS. 10A and 10B illustrate examples of a conductance spectra for the 5^(th) harmonic and overtones at different relative humidity detected in accordance with aspects of the disclosure;

FIGS. 11A-11E illustrate examples of graphs of QCM-D measurements in accordance with aspects of the disclosure;

FIGS. 12A and 12B illustrate examples of graphs of optical measurements in accordance with aspect of the disclosure in different relative humidity;

FIG. 13A illustrates an example of a relative humidity sequence used to acquire properties in accordance with aspects of the disclosure;

FIG. 13B illustrates an example of frequency-dependent magnitude of impedance (Z) measured over the sequence shown in FIG. 13A in accordance with aspects of the disclosure;

FIG. 13C illustrates an example of frequency-dependent phase measured over the sequence shown in FIG. 13A in accordance with aspects of the disclosure;

FIG. 13D illustrates an example of an impedance response in Nyquist plot representation of real and imaginary part of electrical impedance as relative humidity increased in accordance with aspects of the disclosure;

FIG. 14A illustrates an example of cyclic voltammetry (C-V) curves measured at 25, 50, and 200 mV/s sweep rates at varying relative humidity in accordance with aspects of the disclosure;

FIG. 14B illustrates an example of the areas of the C-V curves in FIG. 14A showing hysteresis in capacitance as a function of relative humidity determined in accordance with aspects of the disclosure;

FIG. 14C illustrates an example of the current-voltage (I-V) characterization in accordance with aspects of the disclosure;

FIG. 15 illustrates an example of DC electrical current response over a sequence of varying relative humidity in accordance with aspects of the disclosure;

FIG. 16A illustrates an example of correlations between measured/calculated properties in accordance with aspects of the disclosure;

FIG. 16B illustrates an example of the predicted error with module specific properties only used for prediction verses the properties of multiple modalities in accordance with aspect of the disclosure;

FIG. 16C illustrates an example of average prediction error when the percentage of samples used for training the model is varied comparing the results of different training input;

FIG. 17 illustrates a table showing properties used in the correlation illustrated in FIGS. 16A-16C;

FIG. 18 illustrates a flow chart of a method for deploying one or more models in accordance with aspects of the disclosure;

FIG. 19 illustrates an example of the expected percent error from predicting using a plurality of models in accordance with aspects of the disclosure;

FIG. 20 illustrates a flow chart of a method of predicting properties based on environmental condition(s) in accordance with aspects of the disclosure;

FIG. 21 illustrates a flow chart of a method of determining acquisition parameters for material properties using the predicted properties in accordance with aspects of the disclosure;

FIG. 22 illustrates a flow chart of a method of predicting environmental condition(s) based on measured/calculated properties in accordance with aspects of the disclosure;

FIGS. 23A and 23B illustrate another flow cell in accordance with aspects of the disclosure;

FIGS. 24A and 24B illustrate another flow cell in accordance with aspects of the disclosure; and

FIGS. 25A and 25B illustrate another flow cell in accordance with aspects of the disclosure.

DETAILED DESCRIPTION

In accordance with aspects of the disclosure, one or more machine learned models may be deployed to predict one or more properties of a material under certain environmental condition(s) and/or predict an environment condition(s) near the material from a measured or calculated one or more properties of the material. Further in accordance with other aspects of the disclosure, the acquisition parameters for acquire values associated with one or more characteristics (or properties) of the material may be adjusted based on previous measured values associated with the same one or more characteristics (properties)(or calculated).

FIG. 1 illustrates multiple characterization modules 10 and multi-modal characterization system in accordance with aspects of the disclosure. The system may be used to generate a dataset for training and testing a plurality of models, selecting the model and the above described prediction. The system may also be used to obtain properties of the material by varying the acquisition properties.

In an aspect of the disclosure the system may comprise one or more characterization modules 10A-10C (collectively “10”). Only three modules 10 are shown in FIG. 1 for descriptive purposes, however, there may be N number of characterization modules.

In an aspect of the disclosure, the characterization modules 10A-10C may include modules for electrical, optical, gravimetric and viscoelastic characterization.

For example, characterization module 10A may be an electrical characterization module (hereinafter “electrical characterization module 10A”). The electrical characterization module 10A may comprise one or more of a source-meter, a frequency response analyzer (such as an impedance analyzer or function generator), a voltage source, and/or a current source. The electrical characterization module 10A may be used to characterize a DC and/or AC response. In some aspects, the electrical characterization module 10A may measure or calculate impedance spectroscopy, current-voltage (I-V), cyclic-voltammetry (C-V), and transistor measurements, current and voltage (such as pulsed voltammetry).

For example, the source-meter may be a Keithley 240. In an aspect of the disclosure, the impedance analyzer may be a Solarton 1260 available from Ametek® Scientific Instruments.

In an aspect of the disclosure, a maximum sweep voltage may be determined automatically based on measured film current and rate of change of current with respect to applied voltage. A specific capacitance may be calculated using the integral of the C-V curve at each bias sweep rate. For impedance measurements, the frequency range and applied AC amplitude may be determined by measuring sample impedance and signal-to-noise ratio (SNR) in impedance spectra. This measurement may be in a known environment for establishing default values or for calibration. In post-processing routines, electrical impedance data may be fitted to extract distribution of relaxation times (DRT).

The electrical characterization module 10A may connected to a processing system 50 via one or more general purpose interface bus (GPIB) ports (identified in FIG. 2 as “interfaces 210”). The type of interface used may depend on the device used for measurement. For example, an electrical characterization device may be configured to use a USB port.

The optical characterization module 10B may comprise an optical spectrometer and a light source such as but not limited to laser coupled through fiber optic cable. In some aspects, the spectrometer may be obtained from Ocean Optics Inc. as model no. USB 4000 Fiber Optic Spectrometer. The spectrometer may be connected to the processing system 50 via a USB port or a serial port (one of the interfaces 210). The settings used to acquire the optical spectra may comprise, but are not limited to, excitation light intensity, wavelength range (also referred to herein as sampling frequency window or sample frequency window) and signal integration time. In accordance with aspects of the disclosure, the acquisition settings (such as described above) may be controlled autonomously using active feedback from previously measured optical spectra. In one aspect, this control may target a stable optical intensity without saturating the photodetectors in the spectrometer. In an aspect of the disclosure, a target maximum spectral intensity may be between 70 and 80% of photodetector saturation. The adjustment may be iteratively performed to maintain the intensity within the target maximum spectral intensity. For example, when maximum spectral intensity measure is greater than 80% of the photodetector saturation, integration time may be decreased by a preset increment. When maximum spectral intensity is below 70% of photodetector saturation, the integration time may be increased by a preset increment. The preset increment may be 100 μs. However, other increments may be used. If the maximum spectral intensity is still outside the target maximum spectral intensity, the integration time may be adjusted again.

The gravimetric and viscoelastic characterization module 10C may comprise a portable antenna analyzer coupled to electrodes. For example, the portable antenna analyzer/network analyzer may be a SARK-110. The portable antenna analyzer may be used to measure conductance and/or impedance. The portable antenna analyzer may be connected to the processing system 50 via a USB port (one of the interfaces 210) or wirelessly WIFI/BLUETOOTH (registered Trademark). The portable antenna analyzer may also be electrically connected with electrodes.

Each of the respective devices in the characterization modules 10A-10C may be controlled by the processing system 50. A block diagram of the processing system 50 is shown in FIG. 2. The processing system 50 may comprise a processor 200, a memory 205 and the interfaces 210. The processor 200 may be a CPU. In other aspects, the processor 200 may be a microcontroller or microprocessor or any other processing hardware such as a FPGA. The processor 200 may be configured to execute one or more programs stored in a memory 205 to execute the functionality described herein. The memory 205 may be ROM and RAM. The memory 205 may be any piece of hardware capable of temporarily or permanently storing data. The memory 205 may store one or more programs for interfacing with the devices in the characterization modules 10. For example, the memory 205 may store a pywinusb Python library to communicate with the portable antenna analyzer. The memory 205 may also include other Python libraries for communication with the devices such as the PySerial and PyVisa Python libraries.

The interfaces 210 may be USB interfaces, GPIB interfaces, serial interfaces, . . . etc. In some aspects, the devices in the characterization modules 10 may communicate with the processing system 50 wirelessly.

The combination of the multiple characterization modules 10 enables multi-functional characterization of a material (such as a film) across an ultra-broadband frequency range as shown in FIG. 3. For example, electrical characterization 300 (also referred to as electrical measurement) of a material may provide information below about 10 MHz, e.g., combination of DC electrical measurements (0 Hz) with electrical impedance (1 MHz-10 MHz). A Gravimetric /viscoelastic Characterization 305 (also referred to as QCM measurement or QCM-D measurement) may provide information between about 5 MHz and about 100 MHz, e.g., multi-frequency QCM-D. Optical characterization 310 (also referred to as optical measurement or spectroscopy measurement) may provide information in the THz range.

FIG. 4 illustrates a schematic example of a set up for multi-modal analysis (lab-on-a-crystal platform). As shown in FIG. 4, a quartz crystal 425 is patterned with electrodes 400 and 405. The electrodes 400, 405 may be gold. The electrodes 400 are deposited on the edges of the quartz crystal 425. This is due to the edges not vibrating and therefore does not impact the QCM-D measurements. The electrodes 400 may be deposited using a dual gun electron beam evaporation. Analyte 412 may be deposited on the quartz crystal as a film 410 (referred to herein as the sample 30). The analyte 412 may be a material for testing such as PEDOT:PSS. The analyte 412 is not limited to any particular analyte and may be any target analyte in a film.

The film 410 may cover the central electrode 405 (used for QCM) and spans the quartz crystal 425 to contact the electrodes 400 (allowing in-plane electrical measurements) as well as the QCM measurements. The electrical characterization module 10A may be connected to the electrodes 400 using electrical leads and paste. As shown, a flow cell cover 420 may have an opening 415 to able light to pass or a fiber optic cable to pass to enabled optical measurement via the optical characterization module 10B. In some aspects, the light may be a halogen lamp. In other aspects, the fiber optic cable may run into the flow cell or be in contact with glass quartz (optical window) as described herein. Optical window may also allow for sample heating and surface activation by infrared (IR) and ultraviolet (UV) light sources mounted outside the chamber

In some aspects of the disclosure, additional characterization modules 10N may be used such as photoluminescence (PL 432), Ramon spectroscopy 434, scanning probe microscopy 436, 438 such as atomic force microscope (AFM). The cantilever 430 for the AFM is shown in FIG. 4.

In accordance with aspects of the disclosure, the processing system 50 may control the environment around the sample (sample environment 450) via one or more environmental control modules 20A-20C. In aspect of the disclosure, one of the environmental control modules (e.g., 20A) may be a humidity generator to control the relative humidity. In an aspect of the disclosure, RH-200 humidity generator (L&C Science) may be used. The humidity generator may be connected to the processing system 50 via a USB (one of the interfaces 210). In other aspects, one of the environmental control modules (e.g., 20B) may control gas/vapor flow for different types of gas. In this aspect, the environmental control module 20B may comprise a mass flow controller such as from Alicat Scientific. The mass flow controller may be connected to the processing system 50 via an RS-232 port (one of the interfaces 210). In another aspects, one of the environmental control modules (e.g., 20C) may control the pressure. In this aspect of the disclosure, the environmental control module 20C may comprise a pressure transducer and a flow valve. For example, the pressure may be controlled using a 651C Digital-Analog Pressure Controller with 972 DualMag pressure transducer and butterfly valve (MKS Instruments).

In other aspects, other environmental control modules may be used such as temperature and light. For example, the temperature may be controlled a precision temperature controller such as Model 250 precision temperature controller (J-KEM Scientific).

In accordance with aspects of the disclosure, the processing system 50 may dynamically control the sample environment 450 using a user-defined sequence or automatically. The user-defined sequence may control temperature, pressure, humidity, gas/vapor concentration, and light intensity for a user-defined period of time, ranging from seconds to days, depending on the kinetics of material response to dynamic environmental changes. The user-defined sequence may be input using a user interface 55. An example of a user interface 55 is shown in FIG. 5. When the processing system 50 automatically determines the sample environment 450, the processor 200 may determine how long each step should last and the conditions of the following step based on previously measured material response. In an aspect of the disclosure, the sample environment 450 may initially start at default conditions. In other aspects, the user may define the initial conditions via the user interface 55. The user interface 55 has an environment control portion 505 and a system control portion 510. In the system control portion 510, the user may input a mass flow for a gas. The system control portion 510 also shows the communication ports (interfaces the modules 10, 20 are connected to).

The user may also use the user interface 55 to input one or more acquisition parameters for the characterization modules 10. In an aspect of the disclosure, the user interface 55 may comprises a plurality of separate portions for the inputs for electrical acquisition parameters (portions 525), the inputs for optical acquisition parameters (portion 520) and inputs for QCM acquisition parameters (portion 515).

For example, in an aspect of the disclosure, the system may acquire a QCM conductance/impedance at a fundamental resonance and a plurality of harmonics. The number of harmonics may be selected based on the application and film 410. For example, the system may acquire the conductance/impedance at odd harmonics. The odd harmonics may be from the 3^(rd) to 17^(th) harmonic (where n represents the harmonic number). In an aspect of the disclosure, the user may define a sample frequency window for the fundamental resonance and each harmonic. For example, the user may input a center frequency for the sample frequency window and a frequency width (e.g., +/a frequency). In an aspect of the disclosure, the width increases for larger harmonics. For example, the width is 8 KHz for n=7 and 18 Khz for n=17. The user may also input the resolution. The user may also input other acquisition parameters for QCM-D such as a number of points to average at each frequency. As the number of points increase, the SNR ratio is increased. In some aspects, certain harmonics (higher harmonics) may be ignored when the SNR is below a threshold.

In an aspect of the disclosure, prior to depositing a material on the quartz crystal 425, the conductance spectra of the quartz crystal may be obtained to confirm the viability of the crystal for characterizing a film 410. The fundamental resonance of the quartz crystal is identified by a manufacturer of the quartz crystal. For example, the fundamental resonance may be 5 MHz. Harmonics of the quartz crystal may also be obtained. The same harmonics may be obtained as will be used in the characterization session such as an experiment. The measurements may be done in air. The acquisition parameters used for this initial characterization may also be used as the initial acquisition parameters once the material such as a film 410 is deposited on the quartz crystal 425.

The user may use the portions 525 to input the electrical acquisition parameters for both DC and AC measurements. For example, for the DC measurements, the user may input the bias, the maximum sweep bias, voltage steps and C-V sweep rate among other parameters. For AC measurements such as impedance and phase, the user may input the start and end frequency. Additional AC acquisition parameters may be the AC amplitude, the DC offset and number of signals to average.

The user may use the portions 520 to input the optical acquisition parameters such as the integration time. Although not specifically shown in FIG. 5, the user may also input the sample frequency window for the optical spectra as well (center wavelength and width). It is noted that on the top of the screen there are module specific drop down windows for the characterization modules 10 and the environmental control modules 20.

The user interface 55 may be co-located with the processing system 50. For example, the processing system 50 optionally may include a display. The user interface 55 may be displayed on the display. In other aspects, the user interface 55 may be installed in another device such as a personal computer, mobile terminal such as a mobile telephone, smartphone or laptop, etc.

In accordance with aspects of the disclosure, the acquisition parameters may be adjusted in real-time based on measured/calculated characteristics of a material such as a film.

FIGS. 6A and 6B show an example of a flow cell 600 in accordance with aspects of the disclosure. The flow cell 600 enables simultaneous measurement of electrical 300, optical 310 and QCM-D film 305 properties under one or more controlled environmental conditions. FIG. 6A shows an opened view of the housing 610. FIG. 6B shows a view of the flow cell cover 420 and housing 610. The cover 420 and housing 610 may be manufactured using additive manufacturing techniques such as 3D printing. The housing 610 may comprise openings 606/608. Opening 606 is for the input of gas/vapor. A pipe or channel may be inserted into the opening 606 (gas tube). Opening 608 is for the output of the gap/vapor from the flow cell 600. A pipe or channel may be inserted into the opening 608. These pipes may be connected to the environmental control module 20. While FIGS. 6A and 6B show one opening 606/608 for input and output, in other aspects of the disclosure, the housing 610 may have multiple pairs of openings 606/608 to enable multiple different environments simultaneously. The flow is shown with arrows in FIG. 6A.

The housing 610 may also have slots 620 configured to receive connectors 600, 605. One connector is for QCM-D measurement 600 and another connector is for electrical measurements 605. In an aspect of the disclosure, the connectors 600, 605 may be BNC connectors. The BNC connectors are inserted into the slots 620. The slots 620 may be dimensioned be maintain a snug fit with the connectors 600, 605 such that the environment within the flow cell 600 is unaffected by the external environmental conditions.

The housing 610 and cover 420 form a compartment for the sample 30. In an aspect of the disclosure, the sample 30 may comprises the quartz crystal 425 and film 410 (under measurement) and the electrodes 400, 405. The sample 30 may be held and suspended in air by a sample holder 625. The holder 625 prevents the sample 30 from contacting the housing 610. The holder 625 is connected to one of the connectors 600, 605. As shown in FIG. 6A, the holder 625 is connected to the connector for the QCM-D 600. However, in other aspects, the holder 625 may be connected to connected 605 (for the electrical measurement). The holder 625 may be a metal bar and have two opens for leads (clips) or wiring from the connector 600. The wires connected to connector 600 are connected to electrodes 405. The electrodes 405 may be connected to the wires via springs 645 for supports.

The flow cell cover 420 may comprise an opening. The opening 415 may be filled with a glass quartz. The opening 415 (with glass quartz) enables optical measurements 310 and other spectroscopy measurements.

FIGS. 6C and 6D show schematic views of two different electrode 400A, 400B configurations for electrical measurements 300. As shown in FIG. 6C, four electrodes are deposited on the edges of the film 410/quartz crystal 425 (shown in yellow). However, as shown, only two of the electrodes 400A are connected to connector 605. In FIG. 6C, each connected electrode has a single projection towards the other electrode. In FIG. 6D, two electrodes 400B are shown at the edge of the film 410/quartz crystal 425 (shown in yellow). These electrodes 400B have a plurality of fingers extending toward the other electrodes 400B. The different electrode configurations 400A/400B may be used depending on the conductance of the film 410. When the conductance of the film 410 is low, electrodes 400B may be used (as shown in FIG. 6D). On the other hand, when the conductance of the film 410 is high, the electrodes 400A may be used (as shown in FIG. 6C). A difference between the two configurations is the distance between the electrodes. In FIG. 6C, the distance is larger than the distance in FIG. 6D. The relative conductance of the film 410 may be apriori known and used to select the electrode configuration for the electrical measurements 300.

As shown in FIG. 6C, the optical measurements 310 may be made using either backscattering 640 (reflectance) or transmittance 642. In a case of optical measurement 310 via backscattering 640, the same fiber optic cable (not shown in FIG. 6C) may be used for both emitting wavelengths and detecting the backscattered (reflected) wavelengths from the film 410/quartz crystal 425. In an aspect of the disclosure, the one end of the fiber optic cable may be in contact with the glass quartz in the opening of the cover 420. The other end of the fiber optic cable may be connected to the spectrometer. The fiber optic cable may be bifurcated. In this aspect of the disclosure, the sample 30 may be held by the holder 625 in close proximity to the opening 415 and parallel to avoid loss of the signal (beam divergence).

In a case where the optical measurements 310 use the transmittance 642 (where a film is transparent to the emitted wavelengths), the housing 610 may have another opening (not shown in FIG. 6A or FIG. 6B) on the bottom directly below the film 410/quartz crystal 425. This other opening may be aligned with opening 415. In an aspect of the disclosure, the other opening may be filled with the same glass quartz. The glass quartz may be used because it has minimal impact on the transmitted light. Also, in this aspect of the disclosure, a photodetector may be positioned below the glass quartz (in contact with). The photodetector may be external to the flow cell 600. The photodetector may be connected to the spectrometer. In this case, the fiber optic cable may only emit wavelengths without detecting the same.

As described above, the electrical 300, optical 310 and QCM-D 305 measurements may be made at the same time. In this aspect, the electrical measurements may be made using the electrode 400A/400B at the edges of the film 410/quartz crystal, the QCM-D measurements 305 may be made using the electrodes 405 at the middle of the film 410/quartz crystal and the optical measurements 310 may be made at a location between the electrodes 405, 400A/400B. Since the measurements are made at different positions, the measurements of one type of property may not impact the measurements of another type of property. For example, since the electrical measurements 300 are made at the edges and the edges do not vibrate, the electrical measurements do not impact the QCM-D measurements 305. The flow cell 600 may be used in a gas or vapor controlled environment, however, when the environment includes liquid, the wires, connectors and holder may be covered with material to provide resistance to damage by the liquids.

In other aspects of the disclosure, a different flow cell 600A may be used when the environment may include liquid flow such as shown in FIG. 23A. As shown in FIG. 23A, the cell 600A has isolated space(s) 2310/2315 (compartments) for the electric connector and the QCM-D connector 605/605. The compartments 2310/2315 are formed by the housing 610C and flow cell cover 420A and the internal walls 2340. The walls 2340 may have small openings 2330 for wires to be inserted. The openings for the wires for the QCM-D measurements are shown in FIG. 23A. The openings for the wires for the electric measurements are not viewable in the angled view as shown in FIG. 23A, but would be similarly dimensioned. The housing 610C also has a ridge 2320 for the QCM holder 625. The sample compartment 2335 is located below the opening 415 in the flow cell cover 420A. The QCM holder 625 holds the sample 30 within the sample compartment 2335 such that the sample 30 does not contact the housing 610C. FIG. 23B shows a schematic view of the flow cell 600A. In this aspect of the disclosure, a gasket 2300 may be used to isolate the liquid channel 2305 from certain other components in the flow cell 600A. The gasket 2300 may be made of polydimethylsiloxane (PDMS). The gasket 2300 may be positioned between the walls 2340. In an aspect of the disclosure, the gasket 2300 may also have small openings for the wires. In other aspects, the wires may run above the gasket 2300.

The opening 415 may be filled with glass quartz. The opening 415 (with the glass quartz) may be used for optical measurements 310. In an aspect of the disclosure, a fiber optic cable may be positioned in contact with the glass quartz for emitting and detecting wavelengths to/from the sample 30.

In other aspects of the disclosure, a flow cell 600B may be used for characterization of a sample 30 with respect to a Reference 30A. As noted above, the sample 30 may include the film 410 and quartz crystal 425. However, the reference may include the quartz crystal 425 without the film 410. The electrodes 400 may be deposited on the edges of the quartz crystal as shown in FIG. 24B.

FIG. 24A shows a view of another flow cell 600B, which shows the top 2420 separate from the bottom 2425. In the figure, the top is inverted to show certain components. The housing 610A may comprise openings 606, 608. Opening 606 is for the input of gas/vapor. A pipe or channel may be inserted into the opening 606 (gas tube). Opening 608 is for the output of the gap/vapor from the flow cell 600B. A pipe or channel may be inserted into the opening 608. These pipes may be connected to the environmental control module. While FIG. 24A show one opening 606/608 for input and output, in other aspects of the disclosure, the housing 610A may have multiple pairs of openings 606/608 to enable multiple different environments simultaneously. The flow is shown with blue arrows in FIG. 24A. The housing 610A may also comprise connector openings 620A. These connector openings are for the BNC connectors for the QCM-D measurements. One connector is used for measurements of the sample 30 (left side of figure) and another connector is for measurements of the reference 30A (right side of figures). The openings 620A may be dimensioned be maintain a snug fit with the connectors such that the environment within the flow cell 600B is unaffected by the external environmental conditions. The housing 610A may also have a pair of openings for the connectors for the electrical measurements 630B, one opening is used for the sample 30 (connector) and one opening is used for the reference 30A (connector).

In an aspect of the disclosure, the housing 610A may having an opening 415 on the top. This opening 415 may be filled with glass quartz. The opening 415 (with the glass quartz) may be used for optical measurements 310. In an aspect of the disclosure, a fiber optic cable 2400 may be positioned in contact with the glass quartz for emitting and detecting wavelengths to/from the sample 30. In an aspect of the disclosure, the reference 30A may be positioned below the sample 30 and aligned. Both may be parallel to the opening 415. In other aspects, the glass quartz may be omitted and the fiber optic cable 2400 may be inserted into the opening 415 and positioned adjacent the sample 30. As shown in FIG. 24B, the same fiber optic cable 2400 may be used to emit the wavelength and detect the backscatter 640 (reflectance). In other aspects, if both the reference 30A and the sample 30 are transparent to the wavelengths emitted, the detector may be separate from the emitter (emitter positioned at the top and detector positioned at the bottom or vice versa).

A reference 30A may be used when the sample 30 is composed of a composite material such as having a nanomaterial. The reference 30 may be a polymer material (which is part of the composite material). In this case, a QCM-D response of the reference 30 may be subtracted from the QCM-D response of the composite material.

In other aspects of the disclosure, the same flow cell 600C may be used to characterize the properties of multiple films 410 at the same time. FIGS. 25A and 25B show another flow cell 600C for characterizing different films 410 at the same time in the same environment. FIG. 25A is a schematic view showing N samples. There are N connectors 600 for the different QCM-D measurements (on the N different films) and N connectors 605 for different electrical measurements (on the N different films). The housing 610B may have N openings on top filled in respectively with glass quartz 2500. Each opening may be used for a respective optical measurement 310 via the fiber optic cable. Similar to above, the optical measurements 310 may be using backscattering 640 or transmittance 642.

FIG. 7 illustrates a flow chart of a method of characterizing a material such as a film 410 using multiple modalities in accordance with aspects of the disclosure under one or more different environmental conditions. The method may comprise the design of the characterization process S700, the initializing of the acquisition parameters and environment S705, the data acquisition S710, real-time feedback S715 and Data analytics S720. The results of the analysis may be outputted S725. In some aspects, the results may be displayed in a graph. In other aspects, the results may be outputted as a text file. In other aspects, the results may be displayed on the user interface 55 in an output portion 530 or on a separate output screen. The characterization modules 10 (electrical, optical and gravimetric/viscoeleastic) may be independently operated. The environmental control modules 20 may also be independently operated.

In some aspects, multiple characterization modules may be controlled to operate at the same time to characterize different properties of the material simultaneously. For example, in some aspects, the electrical characterization module 10A and the gravimetric/viscoeleastic characterization module 10C may acquire property values at the same time. In other aspects, electrical characterization module 10A, the optical characterization module 10B and the gravimetric/viscoeleastic characterization module 10C may acquire property values at the same time. In other aspects, the optical characterization module 10B and the gravimetric/viscoeleastic characterization module 10C may acquire property values at the same time. In other aspects, each characterization module 10 may acquire property values at different times. The acquisition of the values may be in one or more different environments. Similar to the characterization modules, the environmental control modules 20 may be independently operated only or in any combination.

The design characterization process (e.g., design of the experiment) may comprise starting one or more programs to execute the functionality described herein and choosing one or more storage locations for the results at S700-1. In an aspect of the disclosure, the acquired/calculated properties may be locally stored. In other aspects, the acquired/calculated properties may be transmitted to a server for storage, and in some aspects, for further processing.

At S700-2, the user may use the user interface 55 to select the relevant modules (e.g., characterization modules 10 and/or environmental control modules 20). For example, the system control portion 510 may have a check box or drop down menu for the selection of the different modules. Different combinations of characterization modules 10 and/or environmental control modules 20) may be selected. For example, the user may select the relative humidity control module (which may include RH-200) in combination with the gravimetric and viscoelastic characterization module 10C (which may include the SARK-110) and electrical characterization module 10A (which may include the Solartron 1260). In the example, in FIG. 5 (the RH-200, SARK-110 and Solartron 1260) are marked with a white box.

At S700-3 and S700-5, the user may use the user interface 55 to select the relevant material properties and input certain acquisition parameters. In an aspect of the disclosure, the acquisition parameters may be mode specific. There may be two modes of acquisition, manual and automatic. In manual mode, the user may set all for the acquisition parameters in advance for each iteration of acquisition. In automatic mode, the user may set the initial acquisition parameters and a sequence of acquisition environments and the processor 200 may calculate successive acquisition parameters for each iteration using the measured values of the material properties (calculated values). The user may set the mode in S700-4 using the user interface 55.

The following description will be made with respect to a selection of the relative humidity environmental control module (including the RH-200). However, this description equally applies to the other environment control modules.

At S700-5, the user may set the sequence of relative humidity used to acquire the material properties. The sequence may include the specific humidity level and the time spent at each humidity level. For example, the setting may include humidity levels every 20% between 20%-80% and a low humidity such as 2% and a high humidity of 95% as shown in FIG. 5. Additionally, the setting may include humidity levels of every 10%. Other sequences may be used. The sequence may be bidirectional, e.g., humidity levels increasing and decreasing. The time at each level may be 20 minutes. In other aspects, the time may be 1 hr.

At S700-5, the user may set the initial acquisition parameters for the gravimetric and viscoelastic characterization module 10C. In aspect of the disclosure, the acquisition parameters may include the number of harmonics, the sampling frequency windows (center frequency and width) and resolution and multiple harmonics for spatial information. For example, highest crystal harmonics correspond to regions closer to the film-crystal interface and lower harmonics correspond to regions closer to the film-environment interface as shown in FIG. 8. N=1 is the fundamental resonance. As noted above, the initial acquisition parameters may be set based on the measured spectra from the quartz crystal 425 alone in the air. However, in an aspect of the disclosure, the initial sampling frequency windows (central frequency and width) may be set based on a knowledge that the film 410 has been deposited. Since the film has a mass, the combined film/quartz crystal will have more mass than just the crystal. Since there is a frequency shift based on a different in mass, the central may be adjusted. It is noted that a decrease in frequency shift Δf corresponds to a mass gain and an in D corresponds to softening of the film (where D is a measure of viscoelastic/stiffness change of the film.

Similarly, at S700-5, the user may set the initial acquisition parameters for the other characterization modules 10A/10B.

Once all of the initial acquisition parameters are set for each characterization module 10 and the environmental control modules 20, the processor 200 may transmit the settings to the respective equipment at S705-1-S705-4. For example, the processor 200 may transmit the set relative humidity to the RH-200, the set sample frequency windows (set of) to the SARK-110, the set frequency ranges and/or bias, step size to the Solartron 1260 and/or Keithley (electrical characterization module 10A) and the optical frequency range, resolution and integrating time to the Ocean Optics (optical characterization module 10B).

At S710-1-S710-3, the respective devices obtain the values for the respective film properties under the set humidity level (e.g., first humidity level). The RH-200 maintains the humidity level at the set point at S710-4. Specifically, the RH-200 causes the set humidity to flow into the flow cell (e.g., 600). At S720-4, the processor 200 determines whether the material response has stabilized, e.g., are the properties changing. When the properties have stabilized, the processor 200 obtains the next environmental condition based on the sequence at S715-4 and controls the RH-200 to produce the next relative humidity at S705-4.

For the gravimetric and viscoelastic characterization module 10C (which may include the SARK-110), the SARK-110 measures the QCM conductance spectra using the sweeping frequencies within the sample frequency window set (plurality of sample frequency windows) received from the processor 200 at a received resolution. In S720-3, the SARK-110 may deconvolute and determine the peaks (e.g., fit peaks). Each measured QCM conductance spectrum may be automatically fitted to a Butterworth-Van Dyke (BVD) lumped-element equivalent circuit model. Information extracted from the BvD model may include frequency shift (change in the resonant peak position Δf) and dissipation change (change in the value of full width at half max of the resonance peak ΔD). Parameters of the fit may be used as initial conditions for the following fit to ensure that initial fitting parameters are close to the convergent parameters. Sometimes the fitting procedure may fail to find a fit because an initial guess for the fitting parameters is too distance from the correct parameters. To alleviate this, best guess for the initial fitting parameter values for the first set of spectra is used, but for subsequent cycles (values), the values for the previous cycle are used as the initial guess. Further, to avoid fitting of distorted resonant peaks, undesirable spurious overtone peaks may be fitted and removed from the resonance peak by subtraction.

Δf and ΔD may be used to estimate changes in elastic modulus and viscosity using one or more models (such as the continuum mechanics viscoelastic model). The calculated properties and fitted peaks may be transmitted to the processing system 50. The processor 200 may store the peaks and calculated properties in the memory 205 in association with the set humidity level. The processor 200 may also display the calculated properties and the recorded conductance spectra. In some aspects, the output may be displayed on the user interface 55 (in the output portion 530).

In other aspects of the disclosure, instead of the gravimetric and viscoelastic characterization module 10C (which may include the SARK-110) performing S720-3, the module 10C may transmit the raw data to the processing system 50 for processing.

At S715-3, the processor 200 may adjust one or more of the acquisition parameters for the QCM-D measurements. In accordance with aspects of the disclosure, dynamic sampling frequency windows 900 are used in each iteration. The dynamic sampling frequency windows 900 track the shifts in the QCM resonance peak(s). The tracking causes the windows 900 to shift to automatically center the respective sampling frequency window(s) 900 on the resonance peak position(s).

The width (e.g., bandwidth) of each sampling frequency window 900 in the window set are iteratively adjusted to accommodate changes in the peak width, ensuring that peak shoulders do not stretch outside the measured frequency range. In QCM spectra which contain spurious overtone peaks adjacent to the main resonant peak, the automated detection of peaks, tracking of peak positions, and prediction of future peak positions may be performed.

FIG. 9A shows an example of one of the dynamic sampling frequency windows 900. The window width is indicated by a red line between the vertical end points. The bottom right corner (first line) represents the initial sampling frequency window. Each line above this bottom, represents the sampling frequency window 900 for the same peak shifting when the humidity is increased. Additionally, as shown, the width of the window in increased as the humidity is increased. This is because the peak becomes wider at higher humidity levels.

In an aspect of the disclosure, the tracking and prediction of future peak positions may be performed using machine learning such as a support vector regression. In some aspects, the SVR may be implemented with Scikit-library in Python. As the number if iterations increase, the model becomes more accurate. FIG. 9B shows the prediction of the peak position for a main peak and spurious overtones. The current detected peaks are identified by a “+” in red, the previous detected peaks are in a blue dot and the predicted peaks are in green circles. The current detected peaks are in the ninth iteration.

In an aspect of the disclosure, the predicted peak positions may be used as a reference for optimizing an initial guess for values of peak parameters during peak fitting and spectral deconvolution. The initial peak fit parameters may be adjusted iteratively as new spectra are acquired and tracked. FIG. 9C shows an example of the peak fitting. The solid dark line represents the peak fitting. Different color filed areas under color lines represent the fitted area of individual peaks which added together they represent complete fit of experimental spectrum in the set frequency window.

In an aspect of the disclosure, a single peak may be separately fitted to avoid interfering from the adjacent spurious peaks (after deconvolution of peaks of interest). FIG. 9D shows an example of the selective peak fitting for one single peak. The red is the fitted single peak whereas the black includes the adjacent spurious peaks.

In accordance with aspects of the disclosure, the same model that is used to predict the peaks, may be used to predict a frequency shift under a different environmental condition (e.g., different humidity level). In other aspect, the data (peak fit parameters as a function of environmental condition) may be used for the calculation of a different regression model. Integration of regression fitting during the active measurements enabled prediction of material response during the next programmed environmental conditions by extrapolation of regression beyond already measured conditions. The regression model may also allow further refinement of the measurement settings, fitting parameters, and sampling window. FIG. 9E shows an example of the modeled results verses the measured data and the predicted Δf . The modeled data is in green, the actual determined Δf is in black and the predicted Δf is in red.

Before transitioning to the next measurement condition (e.g., humidity level), the processing system 50 may assess material response stability by performing event detection (ED) analysis. The ED analysis is based on monitoring changes in moving averages and variance of the Δf response. FIG. 9F shows an example ED analysis. The red curve in FIG. 9C is the measured Δf , and the blue curve is the calculation of the probability of a dynamic event. ED probability may also be used as a reference to determine the threshold at which the material under test was responsive to small changes in dynamic environment.

FIG. 10A shows an example of the Δf response (peak position shift) and spreading for the 5^(th) harmonic and its spurious overtones at 5 different relative humidity, 2%, 20%, 40%, 60%, 90%. As can be seen, at 2%, the peaks are sharp. However, as the humidity rises, the peaks spread out. Also can be seen, the first peak peak-1 is shifted outside the window. FIG. 10A also shows an example of fitting verses the actual measured spectra, where the fitting is shown in a solid black line. FIG. 10B shows an example of the conductance spectra for the 5th harmonic and the same two spurious overtones at different relative humidity. As can be seen in FIG. 10B, the magnitude of the main peak is significantly lower as the relative humidity increases and widens.

Once the acquisition parameters are adjusted by the processor 200, the processor transmits the new parameter to the gravimetric and viscoelastic characterization module 10C (which may include the SARK-110) to use for the next measurements. S705-3, S710-3, S720-3 and S715-3 may be iteratively repeated for each environmental setting.

At S725, the processor 200 may generate and display one or more reports for the QCM-D measurements. FIGS. 11A-11E illustrate examples of reports or graphs that may be created for QCM-D. FIG. 11A shows an example of the shear modulus μ and ΔD and viscosity η. The 3D graph shows all possible solutions for the variables and delta D and delta f. The dark line on the surface shows the trajectory.

FIG. 11B illustrates an example of the Δf normalized by harmonic verses time. FIG. 11C illustrates an example Δf normalized by harmonic verses harmonic and time. FIG. 11B illustrates an example of the ΔD verses time. FIG. 11C illustrates an example ΔF verses harmonic and time.

The processor 200 may control the optical characterization module 10B similar to the gravimetric and viscoelastic characterization module 10C. For example, at S705-2, the processor 200 may transmit to the optical characterization module 10B (which may include the Fiber Optic Spectrometer) the optical sample frequency window, the resolution and the integration time. At S710-2, the Fiber Optic Spectrometer may transmit light within the optical sample frequency window and measure the films 410 transmittance or reflectance spectra. At S720-2, the Fiber Optic Spectrometer deconvolute the measured data and determine the peaks. The spectrometer may also calculate the SNR. The deconvolution and peak fitting may be similar to described above, where each peak may be fit to a Gaussian distribution function and the peak position, max height, and width (FWHM) may be extracted. Default values for the initial fitting parameters for the first fit may be used, and at subsequent cycles of measurements of the optical spectra, calculated fit parameters of the previous spectrum may be used as an initial guess for the fit parameters of the current spectrum.

The calculated properties (such as SNR) and fitted peaks may be transmitted to the processing system 50. The processor 200 may store the peaks and calculated properties in the memory 205 in association with the set humidity level. The processor 200 may also display the calculated properties. In some aspects, the output may be displayed on the user interface 55 (in the output portion 530).

In other aspects of the disclosure, instead of the optical characterization module 10B (which may include the Fiber Optic Spectrometer) performing S720-2, the module 10B may transmit the raw data to the processing system 50 for processing.

At S715-2, the processor 200 may adjust one or more of the acquisition parameters for the optical spectra acquisition. In accordance with aspects of the disclosure, dynamic sample frequency windows are used in each iteration. The dynamic sampling frequency windows track the shifts in the optical spectra peaks. The tracking causes the windows to shift to automatically center the respective sampling frequency window(s) on the peak position(s).

In some aspects, the width (e.g., bandwidth) of the optical frequency window may be iteratively adjusted to accommodate changes in the peak width. In an aspect of the disclosure, the peaks may be detected, tracked and used to predict future peak positions. In an aspect of the disclosure, the tracking and prediction of future peak positions may be performed using machine learning such as a support vector regression. In some aspects, the SVR may be implemented with Scikit-library in Python. In an aspect of the disclosure, the predicted peak positions may be used as a reference for optimizing an initial guess for values of peak parameters during peak fitting and spectral deconvolution. The initial peak fit parameters may be adjusted iteratively as new spectra are acquired and tracked.

Also, in accordance with aspects of the disclosure, the same model that is used to predict the peaks, may be used to predict a reflection/transmittance under a different environmental condition (e.g., different humidity level). In other aspect, the data (peak fit parameters as a function of environmental condition) may be used for the calculation of a different regression model. Integration of regression fitting during the active measurements enabled prediction of material response during the next programmed environmental conditions by extrapolation of regression beyond already measured conditions. The regression model may also allow further refinement of the measurement settings, fitting parameters, and optical frequency sampling window.

Further, as described above, the integration time may be adjusted iteratively to maintain the intensity with the target maximum spectral intensity. This adjustment may be made by the processor 200 based on the calculated SNR. A low SNR may increase the integration time whereas a high SNR may reduce the integration time.

Once the acquisition parameters are adjusted by the processor 200 (e.g., optical frequency sample window and integration time), the processor 200 may transmit the new acquisition parameters to the optical characterization module 10B (which may include the Fiber Optic Spectrometer) to use for the next measurements. S705-2, S710-2, S720-2 and S715-2 may be iteratively repeated for each environmental setting.

At S725, the processor 200 may generate and display one or more reports for the optical measurements. FIGS. 12A and 12B are examples of reports or graphs generated by the processor 200 for the optical characterization. FIG. 12A shows changes in the intensity of reflected light (reflected from the QCM electrode (traveled through a thin film of humidity sensitive material twice) as a function of increased humidity inside the flow cell.

FIG. 12B shows results of analysis of changing optical spectrum of thin film which were fitted, presented as a change in the intensity of peak and peak position at different humidity levels. As shown in FIG. 12B, both the intensity and maximum wavelength may be dependent on the relative humidity. As shown in FIG. 12B, the intensity may decrease with an increase in humidity whereas the wavelength associated with the maximum intensity may increase with an increase in humidity.

The processor 200 may control the electrical characterization module 10A similar to the other modules. For example, when the measured property is an impedance, the processor 200 may transmit the initial acquisition parameters to an impedance analyzer such as the Solartron 1260 at S705-1. The initial acquisition parameters may include the frequency range (start and end) and the number of point (step size). The initial acquisition parameters may also include an AC amplitude and offset (DC). At S710-1, the impedance analyzer may use the initial acquisition parameters to measure the impedance of the film 410 under the first environmental condition, e.g., 2% relative humidity. The impedance analyzer may transmit the measured impedance over the frequency range to the processing system 50. At S715-1, the processor 200 may determine whether to adjust the acquisition parameters based on the measured impedance. In an aspect of the disclosure, the tracking of the impedance and prediction of future impedance may be performed using machine learning such as a support vector regression. For example, the machine learning model may be trained using the samples responses measured at an initial set of environmental conditions, for example, the impedance measured at 2%, 4% and 6% relative humidity. The trained model may then be used to predict the expected impedance of the sample, at e.g., any relative humidity value inside the range from 0-100% relative humidity.

Of note, the low frequency impedance provides good characterization information of the film 410. Therefore, in an aspect of the disclosure, the start frequency of the frequency sweep for the impedance and step size may be adjusted based on a prior measurement (previous iterations).

FIG. 13A shows an example of the change in the environment around the film 410 during impedance measurement. During the first half of the period, the relative humidity increased and during the second half of the period, the relative humidity decreased. The steps may be set by the user as described above. The same relative humidity may be used for each characterization module 10. Both the magnitude (Log (Z) and phase) may change under different environmental conditions (e.g., relative humidity). FIGS. 13B and FIG. 13C show the dependency on relative humidity where FIG. 13B shows an example of a frequency-dependent magnitude of impedance in different relative humidity and FIG. 13C shows an example of a frequency-dependent phase in different relative humidity. FIG. 13D shows a combination of an example real/imaginary part of impedance as a function of humidity. FIG. 13A illustrates the reversibility of impedance response of the film which underwent through changes in the humidity (steps going up and down). The symmetry of the response may be used to characterize reversibility of changes of impedance. If signal appears to be unusually noisy, it may be a result of ongoing changes in impedance, a sign that sample is not stabilized yet. Thus, the condition of the experiment may be adjusted to give more time for stabilization. Further, impedance acquisition adjustment may be done when it's measured for two extreme conditions, one very low humidity, other very high. The adjustment may be made in preference of low AC modulation voltage to avoid artifacts and ensure that the measured current is large enough to be detected by the impedance analyzer.

Once the acquisition parameters are adjusted by the processor 200 (e.g., start frequency and step size) the processor 200 may transmit the new acquisition parameters to the impedance analyzer to use for the next measurements. S705-1, S710-1, and S715-1 may be iteratively repeated for each environmental setting.

At the same time of the impedance measurement, other electrical properties of a film 410 may also be acquired. In other aspects, the other electrical properties of the film 410 may be separately acquired. For example, cyclic-voltammetry (C-V) and current-voltage (I-V) responses may be acquired. The response may be measured by a source-meter. As S705-1, the processor 200 may transmit the initial acquisition parameters to the source-meter. The initial acquisition parameters may include the sweep rate. In an aspect of the disclosure, multiple sweep rates may be used. For example, three different sweep rates may be used. The number of different sweep rates is not limited to three and other different sweep rates may be used. The sweep rates may be 25, 50 and 200 mV/s. However, the sweep rates are not limited to the listed rates such as shown in FIG. 5. The listed rates are only provided for descriptive purposes. The voltages may be applied via the electrodes 400 (which are connected to the source-meter). The sweep rate may be fixed (three different ones) for all of the environmental conditions (e.g., plurality of relative humidity). Other initial acquisition parameters may include the bias (constant and maximum) and voltage steps.

At S710-1, the source-meter may measure the current through the film at the first environmental condition (e.g., 2% relative humidity) for the three different sweep rates and different bias within the maximum bias. The current may be measured via the electrodes 400.

At S720-1, the source-meter may transmit the measured current along with information of the acquisition parameters to the processing system 50 for processing. At S720-1, the processor 200 may calculate the capacitance using the measured current and bias voltage.

At S715-1, the processor 200 may adjust the acquisition parameters for the next iteration based on the measured current or the calculated capacitance. For example, the processor 200 may change the maximum bias based on the measured current or the calculated capacitance. In an aspect of the disclosure, the tracking of the current, capacitance and prediction of future current and capacitance may be performed using machine learning such as a support vector regression. For example, the machine learning model may be trained using the sample responses measured at an initial set of environmental conditions, for example, the capacitance or current measured at 2%, 4% and 6% relative humidity. The trained model may then be used to predict the expected capacitance of the sample or current flowing in the sample at e.g., any relative humidity value inside the range from 0-100% relative humidity. In an aspect of the disclosure, the max bias may be modified in order to increase/decrease the resultant current. For example, when a measured current is high, the max bias may be reduced and vice versa.

FIG. 14A-14C show an example of the C-V curves, capacitance at different relative humidity and I-V at different relative humidity. FIG. 14A shows the C-V curves for three different sweeps at different relative humidity. Relative humidity was varied from 2% (gray curves) to 95% (blue curves).

Measured current may increase as relative humidity increases from 2% to 95%, which is consistent with results from impedance spectroscopy.

FIG. 14B shows the capacitance calculated by integral of the curves from FIG. 14A. Areas of the C-V curves which were used to calculate capacitance in the film are plotted against relative humidity. The example shows dependence of capacitance on sweep rate and relative humidity and reveal hysteresis in capacitance as relative humidity may be cycled from 2% to 95% and back to 2%.

FIG. 14C shows an example of an I-V response of the film 410 as a function of relative humidity level as bias swept from −2.5 V to +2.5 V at constant rate of 200 mVs. The example also shows strong asymmetry between negative and positive biases and complete reversibility in current response as relative humidity was cycled from 2% to 95% and back to 2%.

At S725, the processor 200 may display results of the electric characterization on a display such as described above, e.g., FIGS. 14A-14C.

Other electrical properties of the film 410 may include a DC electrical response (e.g., current). The acquisition parameter for the DC electrical response may be a DC bias (voltage). The processor 200 at S705-1 may transmit to the source-meter the set DC bias. The set DC bias may be defined in S700-5 by the user. For example, the DC bias may be 10 mV. However, other bias may be used. At S710-1, the current is detected by the source-meter (as a function of the relative humidity). The same relative humidity sequence may be used as described above. The current may be measured after the environment stabilizes, e.g., reaches the target humidity and remains for a set time.

FIG. 15 shows an example of the current response as a function of the relative humidity (and time). As can be seen in FIG. 15, as the relative humidity increases, the measured current may increase, however the increase may be nonlinear (shown inset on the top left). In the figures, the bars represent noise level in the measurements, e.g., measurement uncertainty, and the two curves represent hysteresis between increasing humidity (e.g., lower curve) and decreasing humidity (e.g., upper curve).

At S720-1, the processor 200 may calculate an exponential decay model to examine kinetics of the change in current at each relative humidity step. In an aspect of the disclosure, the current response may be automatically fitted to an exponential decay model. This is shown the center of FIG. 15 as the red curves. The processor 200 may also calculate a time constant (τ) of the decay. Values of τ may be plotted in the upper right inset of FIG. 15 as relative humidity increases (black points) and decreases (red points).

In an aspect of the disclosure, the automated analysis of the DC electrical current response to changing relative humidity enables rapid extraction of electrical hysteresis information (upper left inset), kinetics of the response (upper right inset), and dependence of electrical conductivity behavior on ambient environment. In an aspect of the disclosure, the red fit lines shown in the center panel may be used to determine the rate(s) of change of the measured current. For example, when the current is changing, the environment, e.g., relative humidity, may be held constant so that the sample response may stabilize. Once the current reaches a stable value and does not change over time, a signal may be sent from the processor to the environmental control module to change the environment, e.g., increase or decrease the relative humidity.

In the above description examples, the film 410 contained PEDOT:PSS.

As described above, one or more machine learned models may be deployed to predict one or more properties of a material under certain environmental condition(s) and/or predict an environment condition near the material from a measured or sensed one or more properties of the material.

Of note, one or more measured or calculated film properties may be correlated with one or more other measured or calculated film properties in different environmental conditions. These correlations enable the properties to be predicted using one or more machine learned models.

In an aspect of the disclosure, the correlations may be quantified using a parameter such as a bivariate (Pearson) correlation coefficient. The coefficient quantifies the relationships between gravimetric/viscoelastic, electrical, and optical humidity properties of a film in response to the different environment conditions (such as relative humidity). FIG. 17 shows a list of measured/calculated properties that may be examined. This list is provided only for an example and other properties may be correlated and examined. Two optical properties, four electrical properties, eight gravimetric/viscoelastic properties were examined with respect to a relative humidity (environmental). FIG. 16A shows an example of the correlations for a dataset acquired in a similar manner as described above for PEDOT:PSS film. Examples of the bivariate coefficients are shown in FIG. 16A separated by functional mode (green=gravimetric/viscoelastic, blue=electrical, red=optical), where blue correlations correspond to strong negative linear correlation, red to strong positive linear correlation, and white to no linear correlation. The correlations are represented on a color bar from −1 to +1 in the lower right corner.

FIG. 16B illustrates an example of the prediction error (percent error) when using a machine learned model to predict gravimetric/viscoelastic (Δμ-1), electrical (IDC), and optical (ΔI) responses to humidity. These features were chosen as prediction targets because of their nonlinearity, strong RH response, and high correlation with other features.

The machine learned model may be a least absolute shrinkage and selection operator (LASSO) linear regression. LASSO regression may be used as a predictive model because it eliminates insignificant regression coefficients to improve interpretability of the regression, enabling simple comparison between the importance of each feature in the model.

FIG. 16B compares the prediction error when the model is trained only with the characterization module specific responses and prediction error when the model is trained with multi-modal responses. For example, on the top of FIG. 16B, the accuracy of model trained only with the optical data (ΔI) is compared with the accuracy of the model trained with data from the optical, electrical, and gravimetric/viscoelastic modules 10. Similarly, on the bottom of FIG. 16B, the accuracy of model trained only with the gravimetric/viscoelastic data (Δμ-1) is compared with the accuracy of the model trained with data from the optical, electrical, and gravimetric/viscoelastic modules 10. Also, from FIG. 16B, one can see that different detections/sensors may be more accurately predicted at different relative humidity (both single mode and multimodal). The module only training is shown in color (red for optical, blue for electrical and green for gravimetric/viscoelastic) and grey for the multimodal training.

In all cases, supplementing training data with data from other measured modalities may significantly improve accuracy of the model prediction. This example sheds light on the benefits of supplementing measured data with other auxiliary features for improving predictive modeling.

FIG. 16C shows an example of the effect of varying the amount of data used for training verses testing.

The accuracy of any machine learned model (using LASSO) described above, depends strongly on the proportion of measured data which is used for training compared to that used for testing.

When 2% of the data may be used for training, the training set may consist of only 4 sets of data (also referred to herein as samples): features measured at e.g., 2%, 32%, 62%, and 92% relative humidity. However, when 50% of the sets of data may be used for training, the training set may consist of one training set for each integer relative humidity value and one testing set for each half-integer relative humidity value. As the percentage of sets of data used for training increases from 2% to 50%, error of the predictive model decreases as expected in the example depicted in FIG. 16C (top). In FIG. 16C, the blue refers to the electrical property, the red refers to optical property and the green refers to the gravimetric/viscoelastic property.

Once again, in all cases in the example, errors are lower when a single functional modality is supplemented with additional functional modalities. For example, when all modalities (electrical, optical, gravimetric/viscoelastic) are used to predict an optical modality (“All to O” in plot legend), error is significantly lower than when only optical modalities are used to predict optical modalities (“O to O” in legend). As shown the blue curve with the open circles is the error in prediction with only optical modalities (error >4%) whereas the blue curve with blue solid circles is the error in prediction with all modalities for the optical modalities (error <2%).

The enhancement in predictive accuracy is shown in the bottom of FIG. 16C Supplementing electrical and optical training sets with auxiliary modalities resulted in ˜3.5-5% increase in prediction accuracy, while supplementing gravimetric/viscoelastic training resulted in ˜1% accuracy improvement. The effect of supplementing training sets with additional functional modalities is highest when the training samples are sparse (<5% of samples used for training).

FIG. 18 illustrates a flow chart for a method of training/testing and deploying the one or more machine learned models. At S1800, the processor 200 obtains the dataset for training and testing. The dataset may be obtained as described above for the QCM-D response, the optical response and the electrical response (measurements and calculations) where the acquisition parameters may be adjusted based on the previous measured responses. The environmental conditions may be controlled to a plurality of set values. For example, for a relative humidity as the environment conditions a subset of relative humidity levels between 0%-100% may be used. As shown in the example depicted in FIG. 16C, less than 5% of data may be used for training.

In an aspect of the disclosure, a plurality of models may be trained with 4% of the data (96% may be used for testing). The difference between the environmental conditions (such as relative humidity between the data points used in training may be the same, e.g., every 10% or every 20%. In other aspects, the difference may be non-linear. The specific percentages used in training may be user defined or based on apriori knowledge of the equipment.

Model testing may be accomplished using 5-fold cross-validation, where 4% of the data set is randomly selected to be used for training, and the remaining 96% of the dataset is used for testing. This process is performed for each model type and each combination of hyperparameters, and repeated 5 times so that a different training dataset is selected each time.

The regression models may include multiple different ML techniques such as support vector regressions, neural networks, ensemble methods, linear methods and tree-based methods. A non-exhaustive list of model algorithms includes automatic relevance determination regression (ardregression), degree-1 polynomial fit (poly1), degree-2 polynomial fit (po1y2), degree-3 polynomial fit (poly3), adaboost decision tree regressor (adaboostregressor), bagging decision tree regressor (baggingregressor), bayesian ridge regression (bayesianridge), elastic net regressor (elasticnet), Huber regressor (huberregrressor), least-angle regression (lars), cross-validated least-angle regression (larscv), lasso regression (lasso), cross-validated lasso regression (lassocv), lasso model fit with least-angle regression (lassolars), cross-validated lasso model fir with least-angle regression (lassolarscv), lasso model fit with least-angle regression using information criterion (lassolarsic), linear regression (linearregression), orthogonal matching pursuit model (orthogonalmatchingpursuit), cross-validated orthogonal matching pursuit model (orthogonalmatchingpursuitcv), passive aggressive regressor (passiveagressiveregressor), ridge regression (ridge), cross-validated ridge regression (ridgecv), stochastic gradient descent regressor (sgdregressor), Thiel-Sen regression (thielsenregressor), decision tree (decisiontreeregressor), random forest (randomforestregressor), extra tree regressor (extratreeregressor), support vector regression with linear kernel (linearsvr), nu support vector regression (nusvr), and support vector regression with radial basis kernel (svr).

The number of different models trained may be set by a user. For example, more than 10 models may be trained, more than 20 models may be trained, more than 30 models may be trained. Each model may be trained using different hyperparameters.

For purposes of the description only, material properties measured at e.g., 2, 10, 20, 50, 80, 85, 90, and 95% relative humidity may be initially used for training a plurality of machine learning regression models for fitting nonlinear response to humidity at S1810.

At S1815, the trained models with a given hyperparameter sets are tested to predict a material response at different relative humidity. In an aspect of the disclosure, the different relative humidity may be from 2% on the low end to 96% at the upper end. The interval between predictions may be 0.5% relative humidity. However, in other aspects of the disclosure, different end points and intervals may be used. The processor 200 may compare the predicted values for the property based on the model(s) with the actual measured value/calculated value for the same property to obtain an error percentage. FIG. 19 shows an example of a mean model error of 30 different machine learning regression models (y-axis). Examples of 26 different material properties are shown (x-axis). The top pixel row and last pixel column correspond to best of feature and best of model, which represent the best score obtained for each feature across all models, and the best score achieved by each model across all features, respectively.

At S1820, certain models may be selected for different properties based on their performance, e.g., error. For example, the model type with a given hyperparameter configuration which performs the best at predicting the test dataset on average over all the cross-validation splits may be selected to be deployed.

The red in FIG. 19 represents a high percent error whereas the blue represents a lower percent error. For example, the regression models “linearregression”, “huberregression” exhibits a low percentage error for most of the 26 different material properties. These models may be selected and associated with the property(ies) for use in future prediction. In an aspect of the disclosure, only the best model may be selected for use with a particle feature. For example, for DD1, the “huberregression” is the best model and therefore, may be deployed for DD1. The huberregression model however is not the best for cvmax50 or impedance at 1 Hz, but rather the Lassocv model is for the cvmax50 and lassolars for impedance at 1 Hz. Therefore, the Lassocv may be selected for the cvmax50 property.

At S1825, each selected model may be is stored in memory 205 with its associated property(ies). In an aspect of the disclosure, when more than one model has a similar prediction accuracy for a particular property, the model that has the highest prediction accuracy for more properties may be selected and stored. In accordance with aspects of the disclosure, deploying models as described herein may achieve a prediction error of under 7% for all material properties and a mean error of ˜3% across all properties. This is because a broad range of models and hyperparameters may be screened as described above.

As described above, one or more machine learned models may be deployed to predict one or more properties of a material under certain environmental condition(s) and/or predict an environment condition near the material from a measured (calculated) one or more properties of the material.

FIG. 20 illustrates a flow chart of a method for predicting one or more material properties from an environment condition based on the stored machine learned models in accordance with aspects of the disclosure. At S2000, the processor 200 receives an environmental condition. For example, the user may input a desired relative humidity to determine the corresponding material properties. In this aspect, the user may use the user interface 55 to input the environmental condition, e.g., relative humidity. In other aspects of the disclosure, an environment condition sensor may be positioned adjacent the material (such as the film 410 on the quartz crystal 425) and detect the environmental condition. For example, a hygrometer may be used. The sensor, e.g., hygrometer, may transmit the detected relative humidity to the processing system 50. For example, the hygrometer may transmit the detected relative humidity via wireless communication. In other aspects, the hygrometer may be connected to the processing system 50 via one of the interfaces 210.

At S2005, the processor 200 receives target or desired properties for prediction. In an aspect of the disclosure, the user may input one or more properties of the material (such as a film 410) via the user interface 55. The properties may be optical, electrical and/or gravimetric/viscoelastic properties.

At S2010, the processor 200 retrieves one or more stored machine learned models based on the input one or more properties. As noted above, the processor 200 may select the best model for specific properties and associate the model with the properties. Thus, at S2010, the processor 200 may use the inputted one or more properties as the key for selection.

At S2015, the processor 200 uses the retrieved one or more machine learned models to predict the inputted one or more properties using the received environmental condition as the input to the models.

At S2020, the processor 200 outputs the predicted values for the one or more properties. In some aspects, the processor 200 may cause the predicted values for the one or more properties to be displayed on the user interface 55. In other aspects, the processor may transmit the predicted values for the one or more properties to a mobile device.

In other aspects of the disclosure, in addition to outputting the predicted values for the properties, the predicted values may be used to determine the acquisition parameters for the same properties. FIG. 21 shows a method of controlling an acquisition (measurement) of properties of the material in accordance with aspects of the disclosure. S2000-S2020 are the same as described above.

At S2100, the processor 200 may determine the acquisition parameters for the one or more properties using the predicted values for the one or more properties. For example, in a case where the properties are a QCM spectra and specifically the fundamental resonance (e.g., Δf1) and certain harmonics with its overtones (e.g., Δf3, Δf5, Δf7, Δf9, Δf11, Δf13, Δf15 Δf17), the processor 200 may determine the sample frequency windows such that each are centered at the predicted frequency, respectively (the fundamental resonance and each respective harmonics). The processor 200 may determine the width of the sample frequency windows such that it includes the predicted frequencies of the overtones.

The processor 200 may transmit the determined acquisition parameters to the respective characterization modules 10. Additionally, the processor 200 may transmit the received environmental condition in S2000 to the environmental control modules (associated with the condition) to maintain the environment at the specific environmental condition. For example, the processor 200 may transmit a target relative humidity to the RH-200.

The characterization modules 10 may obtain the measurements in a similar manner as described above using the received acquisition parameters and may calculate other properties as needed. The characterization modules 10 may transmit the actual measured properties (values) and calculated properties (values) to the processing system 50. At S2105, the processor 200 receives the actual measured properties (values) and calculated properties (values) and may compare the received values with the predicted values for the same properties (which is stored in the memory 205) to determine a percent error for each property (value). The received values are also stored in the memory 205.

Once the actual percent error is determined for each property, the processor 200 may retrieve the expected percent error (predicted) associated with a respective model used for each property. For example, if the Huberregression model was used to predict the fundamental resonance (frequency shift) (Δf1), the processor may retrieve the expected percent error for the same (an example of which is shown in FIG. 19). The expected percent error for (M1) (e.g., about 1%). At S2110, the processor 200, for each predicted/actual property (value), may determine whether the actual percent error is greater than the predicted percent error. When the actual percent error is greater than the predicted percent error (YES at S2110), the processor 200 may generate an alert. In an aspect of the disclosure, the alert may be displayed on the user interface 55 at S2115. In other aspects of the disclosure, contact information may be prestored in the memory 205. The contact information may be an email address and/or a telephone number of the user (such as a researcher). The telephone number may be used to send a text message or a voice message to a mobile device such as a mobile telephone (smart phone). The email address may be used to send an email to the user. For example, if the actual percent error for the (Δf1) is 5% (which is greater than about 3%), the processor 200 may email or text the alert (or generate a voice alert) at S2115. The alert may include the actual measured or calculated property (value), the predicted measured or calculated property (value), actual percent error, expected percent error and the difference.

Similarly, when the Ardreggression model is used, for example, to predict Δμ11 (11^(th) harmonic), the processor 200 may retrieve the expected percent error of (e.g., about 1%) and compare with the actual percent error determined from the calculated value (from the actual measurements).

In other aspects of the disclosure, the user may set an error tolerance. For example, the user may set a 1% tolerance such that if the difference is less than 1%, the alert is not sent, but if the difference is greater than or equal to the tolerance %, the alert is sent.

If at S2110, the processor 200 determines that the actual percent error is less than or equal to the expected percent error, the processor 20 may cause the measured/calculated properties (values) to be displayed at S2115. The values may be displayed on the user interface 55.

The processor 200 may then calculate new acquisition parameter based on the actual measured/calculated properties as described above (and transmit the same to the characterization modules 10).

In an aspect of the disclosure, when the actual percent error is greater than the expected percent error (and the alert is sent), the processor 200 may stop the acquisition process. This may allow the user to check on the film 410/quartz crystal 425 (and other equipment).

FIG. 22 illustrates a flow chart of a method for predicting one or more environment conditions from one or more material properties based on the stored machine learned models in accordance with aspects of the disclosure. In accordance with aspects of the disclosure, the film 410 may be used as an environmental sensor. At S2105, the processor 200 may receive one or more measured/calculated properties (values) of film from respective characterization module(s) 10. At S2200, the processor 200 may receive one or more environmental conditions from the user. In an aspect of the disclosure, the user may use the user interface 55 to input the one or more environmental conditions (target environmental conditions). For example, the user may input a relative humidity.

At S2010, the processor 200 may retrieve from the memory 205 one or more models associated with both the inputted target environmental condition(s) and the received measured/calculated properties. As noted above, the processor 200 may select the best model (lowest expected percent error) for the received one or more properties (values). The processor 200 may use the received one or more properties as the key for selection (and environmental condition(s)).

At S2205, the processor 200 may use the retrieved one or more machine learned models to predict the inputted one or more environmental conditions using the received one or more properties (values) as the input to the models. For example, the processor 200 may predict the relative humidity around the film 410/quartz crystal 425.

At S2210, the processor 200 outputs the predicted values for the one or more environmental conditions, respectively. In some aspects, the processor 200 may cause the predicted values for the one or more environmental conditions to be displayed on the user interface 55. In other aspects, the processor 200 may transmit the predicted values for the one or more environmental conditions to a mobile device.

As used herein terms such as “a”, “an” and “the” are not intended to refer to only a singular entity, but include the general class of which a specific example may be used for illustration.

As used herein, terms defined in the singular are intended to include those terms defined in the plural and vice versa.

References in the specification to “one aspect”, “certain aspects”, “some aspects” or “an aspect”, indicate that the aspect(s) described may include a particular feature or characteristic, but every aspect may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same aspect.

Various aspects of the present disclosure may be embodied as a program, software, or computer instructions embodied or stored in a computer or machine usable or readable medium, or a group of media which causes the computer or machine to perform the steps of the method when executed on the computer, processor, and/or machine. A program storage device readable by a machine, e.g., a computer readable medium, tangibly embodying a program of instructions executable by the machine to perform various functionalities and methods described in the present disclosure is also provided, e.g., a computer program product.

The computer readable medium could be a computer readable storage device or a computer readable signal medium. A computer readable storage device may be, for example, a magnetic, optical, electronic, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing; however, the computer readable storage device is not limited to these examples except a computer readable storage device excludes computer readable signal medium. Additional examples of the computer readable storage device can include: a portable computer diskette, a hard disk, a magnetic storage device, a portable compact disc read-only memory (CD-ROM), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical storage device, or any appropriate combination of the foregoing; however, the computer readable storage device is also not limited to these examples. Any tangible medium that can contain, or store, a program for use by or in connection with an instruction execution system, apparatus, or device could be a computer readable storage device.

A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, such as, but not limited to, in baseband or as part of a carrier wave. A propagated signal may take any of a plurality of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium (exclusive of computer readable storage device) that can communicate, propagate, or transport a program for use by or in connection with a system, apparatus, or device. Program code embodied on a computer readable signal medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

The terminology used herein is for the purpose of describing particular aspects only and is not intended to be limiting the scope of the disclosure and is not intended to be exhaustive. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the disclosure. 

What is claimed is:
 1. An integrated multifunctional environmental characterization system (IMECS) comprising: a memory configured to store one or more machine learned models correlating one or more environmental conditions adjacent a thin film with one or more properties of the thin film, where the one or more properties comprise one or more properties from at least one of gravimetric/viscoelastic, electrical or optical properties groups; one or more interfaces; a processor configured to: predict an environment condition adjacent to the thin film using the one or more machine learned models from one or more measured properties of the thin film received via the one or more interfaces; and/or predict values for one or more properties of the thin film using the one or more machine learned models from an environmental condition received via one of the one or more interfaces; and display the predicted environment condition and/or the predicted one or more properties.
 2. The IMECS of claim 1, further comprising: a quartz-crystal microbalance (QCM) comprising a quartz crystal, the quartz crystal having a face onto which the thin film is deposited; and electrodes disposed on the face of the quartz crystal to be at least partially covered by the thin film, wherein the processor is configured to interface with two or more characterization modules, the two or more characterization modules are selected from a group consisting of an optical characterization module, an electrical characterization module and a gravimetric/viscoelastic characterization module, the optical characterization module configured to measure at least an optical spectra associate with the thin film, the electrical characterization module configured to measure at least one of I-V, C-V, or impedance associated with the thin film, and the gravimetric/viscoelastic characterization module configured to measure at least QCM conductance spectra associated with the thin film, wherein the processor is configured to control acquisition parameters of at least one corresponding characterization module based on the predicted values of the one or more properties of the thin film.
 3. The IMECS of claim 2, wherein the processor is further configured to receive, via the one or more interfaces, actual measured values for the one or more properties of the thin film and compare the actual measured values with the predicted values, respectively, and wherein when an actual measured value deviates from the predicted value for the same property by more than a prediction error for the model used and the property, the processor is configured to generate an alert.
 4. The IEMCS of claim 3, wherein the alert is transmitted to a registered address, where the register address is selected from a group consisting of an email address or a phone number.
 5. The IEMCS of claim 1, further comprising: a quartz-crystal microbalance (QCM) comprising a quartz crystal, the quartz crystal having a face onto which the thin film is deposited; and electrodes disposed on the face of the quartz crystal to be at least partially covered by the thin film, wherein the processor is configured to: interface with two or more characterization modules, the two or more characterization modules are selected from a group consisting of an optical characterization module, an electrical characterization module and a gravimetric/viscoelastic characterization module, the optical characterization module being configured to measure at least an optical spectra associate with the thin film, the electrical characterization module being configured to measure at least one of I-V, C-V, or impedance associated with the thin film, and the gravimetric/viscoelastic characterization module being configured to measure at least QCM conductance spectra associated with the thin film, interface with one or more environmental control modules, wherein the system further comprises: a fluid-flow cell arranged to encompass the quartz crystal and the thin film, the fluid-flow cell being configured to maintain a controlled environmental condition around the quartz crystal and the thin film and circulate a fluid comprising gas, vapor, liquid and/or a combination thereof adjacent to the thin film in conjunction with the one or more environmental control modules, and wherein the processor is configured to acquire a dataset for training and testing machine learning modules by controlling the one or more environmental control modules to sequentially provide a plurality of different environmental conditions while also acquiring values for one or more properties of the thin film.
 6. The IEMCS of claim 5, wherein the dataset for training and testing contains values obtained from the optical characterization module, the electrical characterization module and the gravimetric/viscoelastic characterization module under different environmental conditions and wherein the processor is configured to adjust one or more acquisition parameters used by each respective characterization module to acquire the values under the plurality of different environmental conditions from baseline acquisition parameters.
 7. The IEMCS of claim 6, further comprising a user interface configured to receive the baseline acquisition parameters.
 8. The IEMCS of claim 6, wherein one of the environmental conditions is relative humidity and wherein the processor is configured to control one of the environmental control modules to provide a plurality of different relative humidity in the fluid-flow cell, starting from a low relative humidity and increasing to a high relative humidity.
 9. The IEMCS of claim 8, wherein the acquired property of the thin film is the QCM conductance spectra, and wherein the plurality of different relative humidity includes a first relative humidity and a second relative humidity, wherein the processor is configured to acquire the QCM conductance spectra for the fundamental resonance and odd harmonics and respective spur overtones at the first relative humidity using a first sampling window set and acquire the QCM conductance spectra for the fundamental resonance and odd harmonics and respective spur overtones at the second relative humidity using a second sampling window set, wherein the width of sampling windows in the second sampling window set is wider than the width of sampling windows the first sampling window set, used to acquire fundamental resonance and odd harmonics and respective spur overtones and wherein the second relative humidity is higher than the first relative humidity, the first sampling window set and the second sampling window set having a plurality of the sampling windows, one sampling window for the fundament resonant and windows corresponding to odd harmonics respectively.
 10. The IEMCS of claim 9, wherein the odd harmonics are from the fundamental resonance to the 17th harmonic, whereby the lower harmonics of the spectral response of the QCD conductance correspond to a shallow region of the thin film near the film-vapor interface and whereby the higher harmonics of the spectral response of the QCM conductance correspond to a deep region of the thin film near the film-quartz crystal interface.
 11. The IEMCS of claim 9, wherein the windows in the first sampling window set and the second sample window set are largest for higher harmonics.
 12. The IEMCS of claim 9, wherein the windows in the first sampling window set and the second sampling window set are shifted in frequency with respect to each other to track peaks of the QCM conductance spectra.
 13. The IEMCS of claim 12, wherein the plurality of different relative humidity comprise a third relative humidity higher than the second relative humidity, wherein the processor is configured to determine the plurality of windows for a third sampling window set by predicting spectral peaks positions of the QCM conductance spectra for the fundamental resonance and odd harmonics and respective spur overtones based on detected and tracked spectral peaks positions of the QCM conductance spectra acquired under the second relative humidity level and the first relative humidity level.
 14. The IEMCS of claim 12, wherein the acquired property of the thin film is one or more electrical properties, and the processor is configured to adjust at least one of minimum frequency used for an impedance measurement, a voltage bias for a DC current response, or a maximum current for a cyclic voltammetry response.
 15. The IEMCS of claim 6, wherein the acquired propriety of the film is one or more optical properties, and the processor is configured to adjust at least one of a sampling window or integration time.
 16. The IEMCS of claim 6, wherein the processor is further configured to: generate a training dataset and a testing dataset from the acquired dataset and the plurality of different environmental conditions; train and test a plurality of models using the training dataset and the testing dataset, where the plurality of models including multiple different machine learning techniques; evaluate a prediction accuracy of each of the plurality of models using an evaluation parameter for one or more properties of the thin film or the environmental conditions; and select the one or more models from among the plurality of models to deploy based on a comparison of the evaluation parameter for each of the plurality of models.
 17. The IEMCS of claim 16, wherein a different model is deployed to predict the value of different properties of the thin film with respect to an environmental condition.
 18. The IEMCS of claim 17, wherein a prediction error for a model is less than 7%.
 19. The IEMCS of claim 6, wherein properties are acquired using at least two different characterization modules at the same time.
 20. An integrated multifunctional environmental characterization system (IMECS) comprising: a quartz-crystal microbalance (QCM) comprising a quartz crystal, the quartz crystal having a face onto which a thin film is deposited; and electrodes disposed on the face of the quartz crystal to be at least partially covered by the thin film, a user interface configured to receive acquisition parameters for two or more characterization modules, the characterization modules are selected from a group consisting of an optical characterization module, an electrical characterization module and a gravimetric/viscoelastic characterization module; and a processor configured to adjust the acquisition parameters used to acquire values of one or more properties of the thin film by each respective characterization module from the received acquisition parameters via the user interface based on measured values for the same properties.
 21. The IMECS of claim 20, wherein the optical characterization module is configured to at least measure optical spectra associate with the thin film, the electrical characterization module is configured to measure at least I-V, C-V, and impedance associated with the thin film, and the gravimetric/viscoelastic characterization module is configured to measure QCM conductance spectra associated with the thin film.
 22. The IMECS of claim 21, wherein the processor is configured: adjust at least one of a sampling window or integration time used by the optical characterization module to measure the optical spectra; adjust at least one of a frequency range or bias used by the electrical characterization module to measure at least I-V, C-V, or impedance; and adjust a plurality of sample windows in a sample window set used by the gravimetric/viscoelastic characterization module to measure the QCM
 23. The IMECS of claim 22, wherein the adjustment of the plurality of sample windows moves to track peaks of the QCM conductance spectra, wherein a main peak within each sample window is maintained at a central position within each sample window.
 24. The IMECS of claim 23, wherein the adjustment changes at least one of the width of a sample window or shifts in frequency the range of the sample window.
 25. The IMECS of claim 23, wherein the width of each sample window for a harmonic is set to include spur overtones of respective harmonic.
 26. The IMECS of claim 23, wherein the plurality of sample windows comprises a sample window for the fundamental resonance and sample windows for a plurality of odd harmonics, where the odd harmonics are from the fundamental resonance to the 17th harmonic, whereby the lower harmonics of the spectral response of the QCD conductance correspond to a shallow region of the thin film near the film-vapor interface and whereby the higher harmonics of the spectral response of the QCM conductance correspond to a deep region of the thin film near the film-quartz crystal interface.
 27. The IMECS of claim 22, wherein the processor is configured to detect, track and predict spectral peak positions of the QCM conductance spectra or the optical spectra based on one or more measurements of the QCM conductance spectra or the optical spectra, respectively, under particular environmental conditions. 